ObjectiveTo systematically evaluate the prevalence of diabetes in the elderly with frailty.MethodsPubMed, EMbase, Web of Science, CNKI, CBM, VIP and WanFang Data databases were electronically searched to collect cross-sectional studies on the prevalence of diabetes in the elderly with frailty from inception to November 2020. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; meta-analysis was then performed by using Stata 15.0 software.ResultsA total of 21 cross-sectional studies involving 22 403 subjects were included. The pooled prevalence of diabetes in the elderly with frailty was 34% (95%CI 26% to 43%). Subgroup analysis showed that the prevalence of diabetes in patients with frailty in Asia and South America was higher than those in Europe and North America. The prevalence of diabetes in patients with frailty using physical frailty measures to evaluate frailty was higher than using multidimensional frailty measures. The prevalence of diabetes in patients with frailty in outpatient and hospital were higher than those in the community.ConclusionsCurrent evidence suggests that the prevalence of diabetes is high in the elderly with frailty.
Systematic reviews and meta-analyses have become the cornerstone methodologies for integrating multi-source research data and enhancing the quality of evidence. Traditional meta-analyses often demonstrate limitations when handling multiple treatment options. Network meta-analysis (NMA) overcomes these limitations by constructing a network of evidence that encompasses various treatment options, allowing for the simultaneous comparison of both direct and indirect evidence across multiple treatment plans. This provides more comprehensive and precise support for clinical decision-making. This article comprehensively reviews the statistical principles of NMA, its three fundamental assumptions, and the statistical inference framework. It also critically analyzes the mainstream NMA software and packages currently available, such as R (including gemtc, netmeta, rjags, pcnetmeta), Stata (mvmeta, network), WinBUGS, SAS, ADDIS, and various online applications, highlighting their strengths, weaknesses, and suitable scenarios. This analysis provides researchers with a scientific and unified framework for conducting clinical studies and policy-making.